A Hybrid Model and Learning-Based Adaptive Navigation Filter

• Published in: IEEE transactions on instrumentation and measurement Vol. 71; pp. 1 - 11
• Main Authors: Or, Barak, Klein, Itzik
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Adaptive algorithm; Artificial neural networks; Covariance; deep neural network (DNN); Drone vehicles; Extended Kalman filter; Global navigation satellite system; global navigation satellite system (GNSS); inertial measurement unit (IMU); Inertial navigation; inertial navigation system (INS); Inertial sensing devices; Kalman Filter; Kalman filters; Machine learning; machine learning (ML); Navigation; Navigation satellites; Navigation systems; Noise measurement; Position errors; quadcopter; supervised learning (SL); unmanned autonomous vehicles; Vehicle dynamics; vehicle tracking
• DOI: 10.1109/TIM.2022.3197775
• ISSN: 0018-9456

The fusion between an inertial navigation system and global navigation satellite systems is regularly used in many platforms, such as drones, land vehicles, and marine vessels. The fusion is commonly carried out in a model-based extended Kalman filter framework. One of the critical parameters of the filter is the process noise covariance. It is responsible for the real-time solution accuracy, as it considers both vehicle dynamics uncertainty and the inertial sensors quality. In most situations, the process noise covariance is assumed to be constant. Yet, due to vehicle dynamics and sensor measurement variations throughout the trajectory, the process noise covariance is subject to change. To cope with such situations, several adaptive model-based Kalman filters were suggested in the literature. In this article, we propose a hybrid model and learning-based adaptive navigation filter. We rely on the model-based Kalman filter and design a deep neural network (DNN) model to tune the momentary system noise covariance, based only on the inertial sensor readings. Once the process noise covariance is learned, it is plugged into the well-established model-based Kalman filter. After deriving the proposed hybrid framework, field experiment results using a quadrotor are presented and a comparison to model-based adaptive approaches is given. We show that the proposed method obtained an improvement of 25% in the position error. Furthermore, the proposed hybrid learning method can be used in any navigation filter and also in any relevant estimation problem.